\section{Conclusion}

In this paper we described our motivations for extracting domain knowledge
regarding good web page design from the domain itself; the world wide web. Our
approach involved the use of combining presently available artificial 
intelligence techniques like Google PageRank and naive bayes models along with
web page data extraction. Over the years, Google PageRank has been proven to be
useful in quantitatively ranking web pages for a given category based on the
citation model. Naive bayes models have been successfully employed in
classification problems like e-mail spam filters and detecting and browsing
events in unstructured text \cite{564391}.

We applied our approach to a case study on politically affliated web pages for
the political parties in the United States of America. We discussed our results
on this case study, and in doing so we were able to describe a model for
classifying web pages for their respective domains.

Our experiments using our approach leads us to believe that it can be used in
classifying web pages based on other properties of the web page that can be
extracted. We also believe that this approach can be used as a basis for
extracting domain knowledge for a given domain where domain experts are not
readily available.
